Intro to PPHA 30538

Peter Ganong and Maggie Shi

January 5, 2026

Course overview

Introduction: Who Are We?

  • Maggie Shi
  • Assistant Professor at Harris since 2023
  • PhD in Economics from Columbia, 2022
  • Health economist thinking about how to reduce waste in healthcare
  • Avid foodie, proud dog mom of Truffles and human mom of Max

Introduction: Who Are We?

  • Peter Ganong
  • At Harris since 2017
  • 28 out of first 30 years of life in Boston (one year doing Jewish studies in Israel, one year at White House Council of Economic Advisers)
  • One year working at City of Boston analytics team
  • Research on labor and public economics, meta analysis
  • Hobbies with my kids: baking, rock climbing, swimming, Jewish learning, Cubs. Just started on coding and triathlons with my oldest…

Roadmap: what you will learn

  • Data extraction, visualization and communication, plus some tech skills
    1. Curriculum
    2. Apply this knowledge to the public sector
  • Course quirks

Step 1: Curriculum

  • Version control (Git and Github) – non-representative low workload this week
  • Advanced visualization curriculum
  • Spatial data (continued)
  • Dashboards
  • Webscraping
  • “Soft skills”

Step 2: public sector: datasets

Data Type Description
Hospital closures CMS
Healthcare fraud HHS OIG
Parking tickets Chicago DOT
Public transportation Chicago DOT
Powerplants EIA

Course Quirks

  1. teaching method – intensive, hands-on support, active participation
  2. self sufficiency
  3. contemporary & realistic over perfect & clean

quirk 1: teaching method – intensive, hands-on support

  1. Mon/Tues afternoon labs: second half is group office hours (time to start working on problem set, and to give and get help).
  2. Wed/Thu/Fri: Additional 4 x 80 minutes group office hours
  3. Practice, practice, practice
    • The problem sets are intentionally long
    • You should expect to commit 10-20 hrs/week to course
    • If you’re spending >20, reach out to your instructor or TA for additional help on being efficient
  4. Awesome teaching assistants

Teaching Team

  • Richard Chen
  • Head TA
  • 2nd Year PhD student

  • Alex Lan
  • Head TA
  • Third year PhD student

Teaching Team

  • Mauro Ttito
  • Instructional TA
  • Second-year MSCAPP

  • Ralph Valery Valiere
  • Instructional TA
  • MPP Alum

Teaching Team

  • Yuxin Chen
  • Instructional TA
  • Second-year MPP

  • Yuri Chang
  • Admin TA
  • Second-year MSCAPP

quirk 1 – active participation required I

  • Active, broad-based participation is key to learning in this class.
  • We ask that you please bring your name tent in to each class
  • Attendance: taken at beginning of class
    • Up to 2 absences with no questions asked
  • For longer-term absences, we can work out a plan with your academic advisor

quirk 1 – active participation required II

  • Most Unclear Discussion cards
    • At the end of each class, please fill out a MUD card with the material in class that was most unclear to you or any other item you were wondering about
    • Name is optional
    • When a topic comes up on multiple MUD cards, we will do a mini lesson about it at lab and/or we will discuss it at the start of the next class

quirk 2: self-sufficiency

Lecture will not cover everything you need to know for problem sets! This is a design choice for the course rather than an accident.

Online

  • Google: “my problem + Python”
  • AI tools

At Harris

  • Lecture, lab, and OH
  • Your classmates
  • Ed – message board for pset questions
  • Harris tutoring

quirk 3: contemporary & realistic over perfect & clean

  • Assignments:
    • In real-life, your manager/audience will ask a plain English question which you need to translate into a query.
    • The course assignments will similarly ask plain English questions.
    • They sometimes will not have a single “correct” answer.
  • Continuously updated material
    • We are constantly updating our material. This means there may be typos. We have processes in place to try to prevent typos in problem sets, but they don’t catch everything.
    • If you think there’s a typo, there probably is. Just ask.
  • Realistic data
    • Data are messy on problem sets, just as is the case in the real world.

Course Overview: Summary

Quirks

  1. teaching method – intensive, hands-on support, active participation
  2. self sufficiency
  3. contemporary & realistic over perfect & clean
  • Any questions?

Course mechanics

Course mechanics: platforms

  • Working on problem sets/project: VSCode and Quarto
    • Note: we will not be using Jupyter Notebooks
  • Distributing lecture material: student Github repository (link)
  • Distributing problem sets and submission for plagiarism checker: Github Classroom

Course mechanics: use Ed to ask questions.

  1. Public Questions
    • Use the problem set-specific categories. Try to avoid repeating already-posted questions.
    • General: logistical details
  2. Private Message
    • Recording/zoom requests

Email discouraged. Will in most cases get a response: “send a PM in Ed”. However, if you have a confidential matter which cannot be shared with the teaching staff, then please email ganong@uchicago.edu or m.shi@uchicago.edu w “30538 confidential”.

Course mechanics: grades

  • 6 problem sets (paired and solo) – 15%.
  • In-lab assessments – 20%. We will drop your lowest assessment grade.
  • In-person final exam (week 8) – 30%. These mirror the in-lab assessments and will be based on the course problem sets.
  • Final group project – 30%
  • Attendance – 5%
  • Extra credit for answering peers’ content questions on Ed – up to 2%

Course mechanics: problem sets and lab assessments

  • 6 problem sets
    • Graded for completion, but expect to spend bulk of time on this.
    • Due at 5pm on Saturdays, solutions and “AI TA” trained on solutions posted immediately after
    • No late submissions
  • Mon/Tue after: 30 minute in-lab assessment
    • Designed to be very closely linked to last week’s problem set
    • Study for these by attending lecture, doing problem set and then, once submitted, comparing your answers to the solution
    • Final follows same structure as lab assessments
  • Wed/Thu after: debrief based on common issues in problem set and assessments

Course mechanics: final project

  • Group formation and proposal mid-quarter
  • Presentations last week of class
  • Code and writeup due during finals
  • More details on this at upcoming lecture

Rhythms during lecture

  • In-class lecture: laptops closed and phones away
    • We encourage you to take notes by hand
    • Or take notes on PDF of slides with a tablet
  • In-class do-pair-share exercises: laptops out
    • Pull the lecture slides to your computer before class so you have the exercise downloaded and ready to go
  • We know this is weird and unnatural, especially for a programming class! These choices are not an accident, they are based on prior experience.

Current sizes by section

As of mid-December, the section enrollments are quite uneven.

  1. Mon 9AM: 29
  2. Mon 10:30AM: 46
  3. Tue 9:30AM: 9
  4. Tue 11AM: 41

Since the course relies heavily on participation, we strongly encourage you to switch to under-enrolled sections. You will have a better experience and learn more.

AI tools and integrity policy

AI tools and integrity policy: roadmap

  • Framework of our approach
  • Summary of integrity policy
  • Attribution

Use of AI

  • Our preferred framework around AI letter written by six teachers here: “Learning to Think in the Age of AI”
  • Prior to Industrial Revolution: work required a lot of physical labor
  • After Industrial Revolution: machines replaced much of this physical effort, but then we had to invent exercise to maintain our physical fitness!
  • AI replaces intellectual effort, so we need to intentionally pursue “cognitive fitness”

Use of AI

  1. Start with your own effort. Read, outline, write your first draft yourself. Try to solve that complex problem on your own without the aid of AI.

  2. Then invite AI in. Ask it to critique your ideas, test your reasoning, ask you questions, show you different perspectives, or let it tutor you on the difficult problem.

  3. Use it to challenge, not to replace. Let it raise the bar, not remove it.

Integrity policy

By having some settings without AI, we are striving to create an environment where AI enhances your knowledge rather than prevents you from true learning.

Setting Python available? Discuss with peers? AI available?
Lecture No No No
In-class exercises Yes No No
Lab assessment & final* Yes No No
Lab pset work Yes Yes No
Outside class and lab Yes Yes Yes

*will use lockdown browser

Attribution

  • For coding assignments (which begin in week 2), if you do use AI, we will ask you to briefly share how you used AI
    • Similar to how you would share this info in workplace
    • Goal is knowledge-sharing: for what tasks do people find AI is helpful vs. a hindrance?

Dealing with ambiguity

If you are unsure of whether a specific action is consistent with this policy, ask.